This series of files compile all analyses done during Chapter 3:

All analyses have been done with R 4.0.2.

Click on the table of contents in the left margin to assess a specific analysis.
Click on a figure to zoom it

⏪ | 🏠 | ⏩

Sources of activity considered for the analyses:

Fisheries data considered for the analyses (expressed as number of fishing events or kilograms of collected individuals for each gear):

Gear Code Years Events Species
Dredge FishDred 2010-2014 21 Mactromeris polynyma
Net FishNet 2010 5 Clupea harengus, Gadus morhua
Trap FishTrap 2010-2015 1061 Buccinum sp., Cancer irroratus, Chionoecetes opilio, Homarus americanus
Bottom-trawl FishTraw 2013-2014 2 Pandalus borealis

1. Spatial variation of exposure indices

Here, we compute semivariograms for each exposure index (on the whole raster, not only extracted values at the stations).

Aquaculture
## Model selected: Sph
## nugget = 0; sill = 0.00742; range = 6.06524; kappa = 0.5

City
## Model selected: Lin
## nugget = 0; sill = 0.0163; range = 8.41153; kappa = 0.5

Sediment dredging
## Model selected: Exp
## nugget = 0.00068; sill = 0.01557; range = 2.67375; kappa = 0.5

Industry
## Model selected: Lin
## nugget = 0; sill = 0.01814; range = 6.79328; kappa = 0.5

Sewers
## Model selected: Sph
## nugget = 0; sill = 0.01603; range = 11.91271; kappa = 0.5

Shipping
## Model selected: Lin
## nugget = 0; sill = 0.06999; range = 4.40942; kappa = 0.5

Fisheries: Dredge
## Model selected: Lin
## nugget = 0; sill = 0.01019; range = 2.81568; kappa = 0.5

Fisheries: Net
## Model selected: Exp
## nugget = 2e-05; sill = 0.00456; range = 0.70613; kappa = 0.5

Fisheries: Trap
## Model selected: Lin
## nugget = 0.00034; sill = 0.00128; range = 1.12045; kappa = 0.5

Fisheries: Bottom-trawling
## Model selected: Lin
## nugget = 0; sill = 0.03509; range = 3.90932; kappa = 0.5

2. Relationships between exposure indices and abiotic parameters

2.1. Covariation

Several types of models were considered to explore relationships: linear, quadratic, exponential and logarithmic. The model with the highest \(R^{2}\) is presented on each plot.

⚠️ Only linear models were implemented for now, as there are some bugs with the calculation of the others.

Aquaculture

City

Sediment dredging

Industry

Sewers

Shipping

Fisheries: Dredge

Fisheries: Net

Fisheries: Trap

Fisheries: Bottom-trawling

Cumulative exposure

2.2. Correlation

Correlations have been calculated with Spearman’s rank coefficient.

Correlation coefficients between exposure indices and ecosystem variables
  om gravel sand silt clay arsenic cadmium chromium copper iron manganese mercury lead zinc S N B H J
aquaculture -0.326 0.145 0.363 -0.343 -0.108 -0.621 -0.693 -0.71 -0.696 -0.599 -0.74 -0.716 -0.698 -0.715 0.373 0.025 -0.029 0.42 0.22
city -0.151 -0.072 0.415 -0.257 -0.12 -0.263 -0.153 -0.186 0.059 -0.031 -0.173 -0.234 -0.182 -0.035 -0.092 -0.008 -0.15 -0.049 0.024
dredging 0.303 -0.122 -0.103 0.118 0.04 0.211 0.14 0.377 0.548 0.616 0.526 0.185 0.271 0.442 -0.156 -0.124 0.03 -0.048 0.016
industry 0.172 -0.119 0.004 0.031 0.059 0.116 0.047 0.302 0.475 0.556 0.479 0.085 0.178 0.348 -0.191 -0.111 0.032 -0.101 -0.009
sewers 0.257 -0.034 -0.346 0.292 0.254 0.616 0.588 0.671 0.68 0.607 0.733 0.589 0.687 0.681 -0.352 -0.058 0.061 -0.387 -0.194
shipping 0.46 -0.251 -0.299 0.315 -0.034 0.526 0.473 0.604 0.677 0.675 0.697 0.53 0.55 0.668 -0.169 -0.067 0.039 -0.146 -0.073
fisheries_dredge -0.238 0.068 0.246 -0.241 -0.045 -0.465 -0.458 -0.558 -0.602 -0.648 -0.649 -0.42 -0.474 -0.578 0.334 0.028 -0.084 0.423 0.228
fisheries_net 0.004 -0.055 -0.158 0.119 0.191 0.078 -0.001 0.055 0.033 0.055 0.106 0.01 0.036 0.026 -0.112 -0.137 0.069 -0.035 0.127
fisheries_trap -0.503 0.158 0.422 -0.38 -0.095 -0.444 -0.346 -0.323 -0.318 -0.291 -0.301 -0.353 -0.376 -0.358 0.077 0.182 -0.032 -0.062 -0.169
fisheries_trawl -0.215 0.172 0.088 -0.182 -0.105 -0.237 -0.306 -0.349 -0.451 -0.368 -0.466 -0.313 -0.308 -0.397 0.216 -0.009 -0.038 0.162 0.032
cumulative_exposure 0.254 -0.097 -0.116 0.142 0.086 0.267 0.153 0.327 0.46 0.491 0.425 0.194 0.334 0.409 -0.072 -0.054 0.019 -0.085 -0.105
p-values of correlation test between exposure indices and ecosystem variables
  om gravel sand silt clay arsenic cadmium chromium copper iron manganese mercury lead zinc S N B H J
aquaculture 0.0005798 0.1356 0.0001144 0.0002802 0.2665 7.874e-13 1.019e-16 8.293e-18 6.586e-17 7.689e-12 5.399e-20 2.963e-18 4.954e-17 3.467e-18 7.175e-05 0.7968 0.7684 6.03e-06 0.0219
city 0.1198 0.4579 8.144e-06 0.007226 0.215 0.005946 0.1137 0.05456 0.5448 0.7521 0.0742 0.01499 0.05964 0.7218 0.3434 0.9372 0.121 0.615 0.8039
dredging 0.001441 0.2084 0.2889 0.2244 0.6842 0.02837 0.1496 5.871e-05 8.644e-10 1.262e-12 5.121e-09 0.05496 0.004503 1.7e-06 0.1066 0.2012 0.7541 0.6201 0.8731
industry 0.07517 0.2204 0.9682 0.7525 0.5423 0.2336 0.6287 0.001477 2.093e-07 4.285e-10 1.622e-07 0.383 0.06579 0.00022 0.0474 0.2517 0.7427 0.3003 0.9224
sewers 0.007329 0.7298 0.0002498 0.002145 0.007908 1.268e-12 2.16e-11 1.947e-15 6.062e-16 3.165e-12 2.031e-19 2.002e-11 2.366e-16 5.388e-16 0.0001859 0.5543 0.5333 3.587e-05 0.04472
shipping 5.587e-07 0.008923 0.001653 0.0009122 0.7234 4.965e-09 2.295e-07 4.656e-12 9.206e-16 1.171e-15 5.419e-17 3.563e-09 6.737e-10 2.923e-15 0.08053 0.4896 0.6886 0.1309 0.4528
fisheries_dredge 0.01314 0.4825 0.01028 0.01193 0.6404 3.925e-07 6.196e-07 3.496e-10 5.797e-12 3.49e-14 2.894e-14 6.176e-06 2.193e-07 5.928e-11 0.0004168 0.7711 0.3857 5.068e-06 0.01743
fisheries_net 0.9713 0.5721 0.1025 0.2201 0.04787 0.4215 0.9885 0.573 0.7361 0.5728 0.2767 0.9196 0.7104 0.7874 0.2496 0.1576 0.4781 0.7212 0.1906
fisheries_trap 2.878e-08 0.1014 5.265e-06 4.889e-05 0.3305 1.481e-06 0.0002478 0.0006488 0.0008039 0.002278 0.001548 0.0001765 6.138e-05 0.0001419 0.4277 0.05927 0.7393 0.524 0.0798
fisheries_trawl 0.02573 0.07593 0.3644 0.05997 0.2811 0.0134 0.001257 0.0002149 9.741e-07 8.969e-05 3.712e-07 0.0009717 0.001194 2.129e-05 0.0248 0.9286 0.6962 0.09349 0.7425
cumulative_exposure 0.007953 0.3202 0.2331 0.1423 0.3777 0.005263 0.1132 0.0005613 5.53e-07 6.626e-08 4.58e-06 0.04408 0.0004186 1.092e-05 0.4576 0.5811 0.8475 0.3817 0.2795

3. Species abundances by cumulative exposure index

The following graphs present the distribution of sampled phyla along a gradient of cumulative exposure.

The threshold classification is based on the exposure index: the higher the index, the lower the status.

Phylum mean abundances by group
Phylum low bad moderate high good
Annelida 12.5 27 27.5 42.1 21.6
Arthropoda 10.8 17 42 66.3 29.4
Cnidaria 0 0 0 0 0.0303
Echinodermata 0.25 0 2.59 2.68 4.55
Mollusca 13.8 5.6 9.34 20.7 10.7
Nematoda 0 0.2 1.72 14.6 13.7
Nemertea 0 0 0.138 0.324 0
Sipuncula 0.5 0 0.483 0.162 0.212

4. Regressions between exposure indices and community characteristics

4.1. Data manipulation

For the following analyses, independant variables are exposure indices, dependant variables are community characteristics. Variables have been standardized by mean and standard-deviation.

All stations and predictors were selected for the regressions, as we are interested in each of them (following graphs are for information only).

Correlation coefficients between exposure indices
  aquaculture city dredging industry sewers shipping fisheries_dredge fisheries_net fisheries_trap fisheries_trawl
aquaculture 1 -0.101 -0.39 -0.374 -0.852 -0.669 0.792 -0.111 0.202 0.62
city -0.101 1 0.269 0.248 0.003 0.165 -0.292 -0.072 -0.022 -0.355
dredging -0.39 0.269 1 0.933 0.542 0.649 -0.598 0.005 -0.117 -0.479
industry -0.374 0.248 0.933 1 0.555 0.537 -0.544 0.082 0.044 -0.525
sewers -0.852 0.003 0.542 0.555 1 0.607 -0.745 0.182 -0.148 -0.556
shipping -0.669 0.165 0.649 0.537 0.607 1 -0.663 0.037 -0.373 -0.619
fisheries_dredge 0.792 -0.292 -0.598 -0.544 -0.745 -0.663 1 -0.08 0.142 0.514
fisheries_net -0.111 -0.072 0.005 0.082 0.182 0.037 -0.08 1 0.135 -0.071
fisheries_trap 0.202 -0.022 -0.117 0.044 -0.148 -0.373 0.142 0.135 1 0.23
fisheries_trawl 0.62 -0.355 -0.479 -0.525 -0.556 -0.619 0.514 -0.071 0.23 1

4.2. Univariate regressions

We used linear models for the regressions on community characteristics. Variables have been standardized by mean and standard-deviation (coefficients need to be back-transformed to be used in predictive models).

We identified which variables were selected after an AIC procedure to predict the best the parameters. Results of the variable selection, according to AIC, are shown on the table below:

Human activity S N B H J
Aquaculture
City
Sediment dredging - +
Industry
Sewers - - -
Shipping
Fisheries: Dredge + +
Fisheries: Net
Fisheries: Trap
Fisheries: Bottom-trawling -
Adjusted \(R^{2}\) 0.17 0.01 0 0.13 0.06

Details of the regressions, with diagnostics and cross-validation, are summarized below.

Richness
## FULL MODEL
## Adjusted R2 is: 0.15
Fitting linear model: S ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap + fisheries_trawl
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.499e-16 0.08891 -6.185e-15 1
aquaculture 0.07318 0.1099 0.6657 0.5072
city -0.07626 0.1149 -0.664 0.5083
dredging -0.01389 0.1115 -0.1245 0.9011
industry -0.06323 0.1368 -0.4624 0.6449
sewers -0.219 0.1313 -1.668 0.09845
shipping 0.1356 0.1018 1.332 0.186
fisheries_dredge 0.2492 0.1002 2.488 0.01455 *
fisheries_net -0.0005094 0.08985 -0.00567 0.9955
fisheries_trap 0.05792 0.1022 0.5667 0.5722
fisheries_trawl 0.1316 0.09454 1.392 0.1672 
## RMSE from cross-validation: 46.27009
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries_dredge fisheries_net fisheries_trap fisheries_trawl
VIF 1.23 1.29 1.25 1.53 1.47 1.14 1.12 1.01 1.14 1.06 

## REDUCED MODEL
## Adjusted R2 is: 0.17
Fitting linear model: S ~ sewers + fisheries_dredge
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -6.547e-16 0.08778 -7.459e-15 1
sewers -0.2817 0.09165 -3.073 0.002698 * *
fisheries_dredge 0.2548 0.09165 2.78 0.006435 * * 
## RMSE from cross-validation: 0.9211796
Variance Inflation Factors
  sewers fisheries_dredge
VIF 1.04 1.04

Density
## FULL MODEL
## Adjusted R2 is: -0.03
Fitting linear model: N ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap + fisheries_trawl
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.246e-16 0.09784 4.339e-15 1
aquaculture -0.06837 0.121 -0.5652 0.5733
city 0.1147 0.1264 0.9073 0.3665
dredging -0.1187 0.1227 -0.9673 0.3358
industry -0.1857 0.1505 -1.234 0.2202
sewers 0.1758 0.1445 1.217 0.2265
shipping -0.1097 0.112 -0.979 0.33
fisheries_dredge 0.02122 0.1102 0.1925 0.8478
fisheries_net -0.04556 0.09888 -0.4608 0.646
fisheries_trap 0.01836 0.1125 0.1632 0.8707
fisheries_trawl 0.03962 0.104 0.3808 0.7042 
## RMSE from cross-validation: 66.15888
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries_dredge fisheries_net fisheries_trap fisheries_trawl
VIF 1.23 1.29 1.25 1.53 1.47 1.14 1.12 1.01 1.14 1.06 

## REDUCED MODEL
## Adjusted R2 is: 0.01
Fitting linear model: N ~ dredging
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.931e-16 0.09561 2.019e-15 1
dredging -0.148 0.09606 -1.541 0.1264 
## RMSE from cross-validation: 1.007331
Variance Inflation Factors
  dredging
VIF 1

Biomass
## FULL MODEL
## Adjusted R2 is: -0.05
Fitting linear model: B ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap + fisheries_trawl
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.779e-16 0.09871 -2.816e-15 1
aquaculture -0.1066 0.122 -0.8731 0.3847
city -0.1521 0.1275 -1.193 0.2357
dredging 0.02227 0.1238 0.1799 0.8576
industry 0.1062 0.1518 0.6997 0.4858
sewers -0.2088 0.1458 -1.433 0.1552
shipping -0.137 0.113 -1.212 0.2284
fisheries_dredge -0.06607 0.1112 -0.594 0.5539
fisheries_net -0.007141 0.09976 -0.07159 0.9431
fisheries_trap 0.01514 0.1135 0.1334 0.8941
fisheries_trawl 0.003222 0.105 0.0307 0.9756 
## RMSE from cross-validation: 8.265183
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries_dredge fisheries_net fisheries_trap fisheries_trawl
VIF 1.23 1.29 1.25 1.53 1.47 1.14 1.12 1.01 1.14 1.06 

## REDUCED MODEL
## Adjusted R2 is: 0
Fitting linear model: B ~ 1
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.205e-17 0.09623 -3.331e-16 1 
## RMSE from cross-validation: 0.9988509

Quitting from lines 403-405 (C3_analyses_B.Rmd) Error in h(simpleError(msg, call)) : erreur d’évaluation de l’argument ‘x’ lors de la sélection d’une méthode pour la fonction ‘t’ : erreur d’évaluation de l’argument ‘x’ lors de la sélection d’une méthode pour la fonction ‘as.data.frame’ : indice hors limites De plus : There were 50 or more warnings (use warnings() to see the first 50)

Diversity
## FULL MODEL
## Adjusted R2 is: 0.11
Fitting linear model: H ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap + fisheries_trawl
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -7.785e-17 0.09099 -8.556e-16 1
aquaculture 0.08597 0.1125 0.7642 0.4466
city -0.1044 0.1175 -0.8879 0.3768
dredging 0.1533 0.1141 1.343 0.1825
industry -0.01545 0.14 -0.1104 0.9124
sewers -0.3325 0.1344 -2.475 0.01506 *
shipping 0.101 0.1042 0.9694 0.3348
fisheries_dredge 0.1792 0.1025 1.748 0.08365
fisheries_net 0.05076 0.09196 0.5521 0.5822
fisheries_trap 0.01163 0.1046 0.1112 0.9117
fisheries_trawl -0.03955 0.09676 -0.4087 0.6836 
## RMSE from cross-validation: 16.43584
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries_dredge fisheries_net fisheries_trap fisheries_trawl
VIF 1.23 1.29 1.25 1.53 1.47 1.14 1.12 1.01 1.14 1.06 

## REDUCED MODEL
## Adjusted R2 is: 0.13
Fitting linear model: H ~ sewers + fisheries_dredge
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.89e-17 0.08971 -4.337e-16 1
sewers -0.2805 0.09366 -2.995 0.003424 * *
fisheries_dredge 0.1963 0.09366 2.096 0.03851 * 
## RMSE from cross-validation: 0.934956
Variance Inflation Factors
  sewers fisheries_dredge
VIF 1.04 1.04

Evenness
## FULL MODEL
## Adjusted R2 is: 0.01
Fitting linear model: J ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap + fisheries_trawl
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.727e-16 0.09587 -2.845e-15 1
aquaculture 0.02333 0.1185 0.1968 0.8444
city -0.06275 0.1239 -0.5067 0.6135
dredging 0.1953 0.1202 1.624 0.1076
industry -0.002176 0.1475 -0.01475 0.9883
sewers -0.2688 0.1416 -1.899 0.06058
shipping -0.009191 0.1097 -0.08374 0.9334
fisheries_dredge 0.05474 0.108 0.5068 0.6134
fisheries_net 0.04791 0.09688 0.4945 0.6221
fisheries_trap -0.04752 0.1102 -0.4312 0.6673
fisheries_trawl -0.15 0.1019 -1.472 0.1444 
## RMSE from cross-validation: 91.35394
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries_dredge fisheries_net fisheries_trap fisheries_trawl
VIF 1.23 1.29 1.25 1.53 1.47 1.14 1.12 1.01 1.14 1.06 

## REDUCED MODEL
## Adjusted R2 is: 0.06
Fitting linear model: J ~ dredging + sewers + fisheries_trawl
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.606e-16 0.09342 -2.79e-15 1
dredging 0.1638 0.09907 1.654 0.1012
sewers -0.2737 0.1003 -2.728 0.007475 * *
fisheries_trawl -0.1422 0.09579 -1.484 0.1408 
## RMSE from cross-validation: 1.026905
Variance Inflation Factors
  dredging sewers fisheries_trawl
VIF 1.06 1.07 1.02

Annelids
## FULL MODEL
## McFadden's pseudo-R2 is: 0.09
Fitting generalized (poisson/log) linear model: annelids ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap + fisheries_trawl
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.345 0.0188 177.9 0 * * *
aquaculture 0.05994 0.02136 2.806 0.005015 * *
city 0.09822 0.02252 4.361 1.292e-05 * * *
dredging -0.1569 0.02993 -5.243 1.582e-07 * * *
industry -0.2086 0.03425 -6.089 1.136e-09 * * *
sewers 0.08524 0.02907 2.932 0.003363 * *
shipping 0.05828 0.01853 3.145 0.001658 * *
fisheries_dredge -0.08875 0.02583 -3.436 0.0005914 * * *
fisheries_net -0.0613 0.02287 -2.681 0.007347 * *
fisheries_trap 0.08082 0.0171 4.725 2.297e-06 * * *
fisheries_trawl -0.2465 0.03327 -7.41 1.264e-13 * * * 
## Unbiased RMSE from cross-validation: 36.48564
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries_dredge fisheries_net fisheries_trap fisheries_trawl
VIF 1.3 1.5 1.25 1.55 1.44 1.13 1.19 1 1.37 1.04 

## REDUCED MODEL
## McFadden's pseudo-R2 is: 0.09
Fitting generalized (poisson/log) linear model: annelids ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap + fisheries_trawl
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.345 0.0188 177.9 0 * * *
aquaculture 0.05994 0.02136 2.806 0.005015 * *
city 0.09822 0.02252 4.361 1.292e-05 * * *
dredging -0.1569 0.02993 -5.243 1.582e-07 * * *
industry -0.2086 0.03425 -6.089 1.136e-09 * * *
sewers 0.08524 0.02907 2.932 0.003363 * *
shipping 0.05828 0.01853 3.145 0.001658 * *
fisheries_dredge -0.08875 0.02583 -3.436 0.0005914 * * *
fisheries_net -0.0613 0.02287 -2.681 0.007347 * *
fisheries_trap 0.08082 0.0171 4.725 2.297e-06 * * *
fisheries_trawl -0.2465 0.03327 -7.41 1.264e-13 * * * 
## Unbiased RMSE from cross-validation: 36.51031
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries_dredge fisheries_net fisheries_trap fisheries_trawl
VIF 1.3 1.5 1.25 1.55 1.44 1.13 1.19 1 1.37 1.04

Arthropods
## FULL MODEL
## McFadden's pseudo-R2 is: 0.17
Fitting generalized (poisson/log) linear model: arthropods ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap + fisheries_trawl
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.632 0.01686 215.4 0 * * *
aquaculture -0.2642 0.02517 -10.5 8.981e-26 * * *
city 0.2199 0.01938 11.35 7.596e-30 * * *
dredging -0.2205 0.0253 -8.715 2.902e-18 * * *
industry -0.5373 0.02999 -17.92 8.988e-72 * * *
sewers 0.5954 0.02366 25.16 1.05e-139 * * *
shipping -0.09925 0.01618 -6.133 8.65e-10 * * *
fisheries_dredge 0.08097 0.01391 5.821 5.845e-09 * * *
fisheries_net -0.09027 0.02028 -4.451 8.549e-06 * * *
fisheries_trap -0.08966 0.01719 -5.216 1.829e-07 * * *
fisheries_trawl 0.01148 0.01623 0.7075 0.4793 
## Unbiased RMSE from cross-validation: 95.792
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries_dredge fisheries_net fisheries_trap fisheries_trawl
VIF 1.17 1.33 1.18 1.82 1.81 1.1 1.09 1 1.22 1.06 

## REDUCED MODEL
## McFadden's pseudo-R2 is: 0.17
Fitting generalized (poisson/log) linear model: arthropods ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.632 0.01685 215.6 0 * * *
aquaculture -0.2646 0.02512 -10.53 6.142e-26 * * *
city 0.2172 0.019 11.43 2.817e-30 * * *
dredging -0.2203 0.0253 -8.705 3.175e-18 * * *
industry -0.5362 0.02993 -17.91 9.222e-72 * * *
sewers 0.5923 0.02321 25.51 1.388e-143 * * *
shipping -0.1014 0.0159 -6.375 1.833e-10 * * *
fisheries_dredge 0.07976 0.01381 5.775 7.703e-09 * * *
fisheries_net -0.09055 0.02028 -4.466 7.976e-06 * * *
fisheries_trap -0.08914 0.01717 -5.191 2.097e-07 * * * 
## Unbiased RMSE from cross-validation: 89.3128
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries_dredge fisheries_net fisheries_trap
VIF 1.16 1.3 1.18 1.81 1.78 1.08 1.08 1 1.22

Molluscs
## FULL MODEL
## McFadden's pseudo-R2 is: 0.19
Fitting generalized (poisson/log) linear model: molluscs ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap + fisheries_trawl
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.459 0.03058 80.41 0 * * *
aquaculture 0.06516 0.02674 2.436 0.01484 *
city 0.1413 0.032 4.415 1.009e-05 * * *
dredging -0.0954 0.04178 -2.284 0.0224 *
industry 0.3211 0.03663 8.766 1.857e-18 * * *
sewers -0.3914 0.04285 -9.135 6.562e-20 * * *
shipping -0.2981 0.04308 -6.919 4.539e-12 * * *
fisheries_dredge 0.1036 0.01878 5.518 3.432e-08 * * *
fisheries_net 0.06904 0.02534 2.724 0.006446 * *
fisheries_trap 0.04718 0.02484 1.899 0.05756
fisheries_trawl 0.01166 0.02586 0.4508 0.6521 
## Unbiased RMSE from cross-validation: 17.50708
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries_dredge fisheries_net fisheries_trap fisheries_trawl
VIF 1.22 1.57 1.51 1.54 1.26 1.19 1.09 1.01 1.34 1.06 

## REDUCED MODEL
## McFadden's pseudo-R2 is: 0.17
Fitting generalized (poisson/log) linear model: arthropods ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.632 0.01685 215.6 0 * * *
aquaculture -0.2646 0.02512 -10.53 6.142e-26 * * *
city 0.2172 0.019 11.43 2.817e-30 * * *
dredging -0.2203 0.0253 -8.705 3.175e-18 * * *
industry -0.5362 0.02993 -17.91 9.222e-72 * * *
sewers 0.5923 0.02321 25.51 1.388e-143 * * *
shipping -0.1014 0.0159 -6.375 1.833e-10 * * *
fisheries_dredge 0.07976 0.01381 5.775 7.703e-09 * * *
fisheries_net -0.09055 0.02028 -4.466 7.976e-06 * * *
fisheries_trap -0.08914 0.01717 -5.191 2.097e-07 * * * 
## Unbiased RMSE from cross-validation: 91.1573
Variance Inflation Factors
  aquaculture city dredging industry sewers shipping fisheries_dredge fisheries_net fisheries_trap
VIF 1.16 1.3 1.18 1.81 1.78 1.08 1.08 1 1.22

🔝